# install.packages(c("tidyverse","purrr","R.matlab","readxl","dplyr"))
#%% PACKAGES FOR STATS
library(readxl);
library(purrr);
library(tidyverse);
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.0 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(tibble);
library(knitr);
library(gtsummary);
## #BlackLivesMatter
library(kableExtra);
##
## Attaching package: 'kableExtra'
##
## The following object is masked from 'package:dplyr':
##
## group_rows
library(lme4);
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
##
## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack
library(MuMIn);
library(car);
## Loading required package: carData
##
## Attaching package: 'car'
##
## The following object is masked from 'package:dplyr':
##
## recode
##
## The following object is masked from 'package:purrr':
##
## some
library(effectsize);
library(sjPlot);
library(emmeans);
## Welcome to emmeans.
## Caution: You lose important information if you filter this package's results.
## See '? untidy'
#%% PACKAGES FOR PLOTS & HTML HANDLING
# library(effects);
# library(sjPlot);
# library(plotly);
# library(webshot)
# library(reshape2);
# library(htmltools)
# library(Polychrome);
# library(htmlwidgets);
# library(shiny)
# library(webshot)
library(scatterplot3d)
library(RColorBrewer)
library(openxlsx)
gtsummary::set_gtsummary_theme(theme_gtsummary_journal("jama"))
## Setting theme `JAMA`
## Setting theme `JAMA`
#--
ispc <- function() {
sys_name <- Sys.info()["sysname"]
if (sys_name == "Windows") {
return(TRUE)
} else {
return(FALSE)
}
}
#%% CLUSTERS TO PLOT
# clusters = c(3,4,6,7,8,9,10,13) # 10172024_MIM_YAOAN89_antsnorm_dipfix_iccREMG0p4_powpow0p3_skull0p01_15mmrej_speed
clusters = c(3,4,5,6,7,8,9,10,11,12,13) # RSup/RSM, PreC, LSM, Mid Cing, LSup, LPPA, RPPA
# clusters = c(3,4,5,6,7,8,9,10,11,12,13) # {'Left Sensorimotor','Right Posterior Parietal','Mid Cingulate', ...
#'Right Cuneus','Right Sensorimotor','Left Supplementary Motor','Right Occipital', ...
#'Left Occipital','Left Temporal','Left Posterior Parietal','Right Temporal'};
#%% EEG PARAMS
#--
eeg_measures = c('theta_avg_power','alpha_avg_power','beta_avg_power');
eeg_title_chars = c("**THETA** Mean","**ALPHA** Mean","**BETA** Mean");
fext = 'speed_manu_tests';
excel_dir <- paste0("/jsalminen/GitHub/MIND_IN_MOTION_PRJ/MindInMotion_YoungerOlderAdults_BrainSpeedChanges/src/r_scripts/eeg_speed_lmes/fooof_spec_table.xlsx")
# excel_dir <- "/jsalminen/GitHub/MIND_IN_MOTION_PRJ/_data/MIM_dataset/_studies/02202025_mim_yaoa_powpow0p3_crit_speed/__iclabel_cluster_kmeansalt_rb3/icrej_5/11/spca_fooof_psd_anl/fooof_spec_table.xlsx"
if(ispc()){
excel_dir <- paste0("M:",excel_dir)
}else{
excel_dir <- paste0("/blue/dferris",excel_dir);
}
orig_eegt <- read_excel(excel_dir,sheet="Sheet1")
#%% SUBSET
orig_eegt <- orig_eegt %>%
select(subj_char,cond_char,group_id,group_char,cluster_id,design_id,
theta_avg_power,alpha_avg_power,beta_avg_power,
);
eegt <- orig_eegt;
eegt$group_name = eegt$group_char
eegt$model_char = eegt$design_id
eegt$cluster_n = eegt$cluster_id;
eegt <- filter_at(eegt,vars('cond_char'), any_vars(. %in% c('0.25','0.5','0.75','1.0')))
flat_speeds = unique(eegt$cond_char)
#%% MUTATE VARIABLES
eegt$speed_cond_num <- as.numeric(eegt$cond_char);
eegt <- mutate(eegt,across(c('subj_char'), factor))
eegt <- mutate(eegt,across(c('group_char'), factor))
eegt <- mutate(eegt,across(c('group_name'), factor))
eegt <- mutate(eegt,across(c('model_char'), factor))
#%% COLORS
color_pal_subj = brewer.pal(9,'PuBuGn')
color_pal_subj = color_pal_subj[5:9];
#%% TBL VALUES
tbl_clusterS = unique(eegt$cluster_n);
tbl_subjects = unique(eegt$subj_char);
tbl_groups = unique(eegt$group_char);
#%% DISPLAY TBL
head(eegt)
dtbl <- eegt;
rm(eegt)
calc_cohensf2 <- function(mod_main,mod_alt){
r2_out = r.squaredGLMM(mod_main);
r2_outalt = r.squaredGLMM(mod_alt);
r2m = r2_out[1] # input your R2
f2m = r2m/(1 - r2m)
r2c = r2_out[2] # input your R2
f2c = r2c/(1 - r2c)
f2m = (r2_out[1]-r2_outalt[1])/(1-r2_out[1]);
f2c = (r2_out[2]-r2_outalt[2])/(1-r2_out[2]);
print(str_glue("r2m: {round(r2m,4)},\tr2c: {round(r2c,4)}\n\n"))
print(str_glue("f2m: {round(f2m,4)},\tf2c: {round(f2c,4)}\n\n"))
vals = data.frame(r2m, r2c, f2m, f2c);
return (vals)
}
#%% EXCEL DATAFRAME
excel_df <- data.frame(cluster_num=double(),
group_char=character(),
model_char=character(),
kinematic_char=character(),
freq_band_char=character(),
mod_num_obs=character(),
coeff_chars=character(),
coeffs=character(),
confint_chars=character(),
emmeans=character(),
emmeans_se=character(),
confint_lwr=character(),
confint_upr=character(),
anv_chars=character(),
anv_pvals=character(),
anv_stats=character(),
anv_dfs=character(),
r2_m_int=double(),
r2_c_int=double(),
f2_m_int=double(),
f2_c_int=double(),
fsq_chars=character(),
fsq_vals=character(),
etasq_chars=character(),
etasq_vals=character(),
ran_effs_char=character(),
ran_effs_n=character())
| Changes in theta_avg_power for Cluster: 3 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 16.2621 1 5.516e-05 ** speed_cond_num 2.1691 1
0.14081
group_char 8.0703 2 0.01768
speed_cond_num:group_char 4.1239 2 0.12720
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model”
## Warning: 'r.squaredGLMM' now calculates a revised statistic. See the help page.
r2m: 0.0731, r2c: 0.8923
f2m: 0.0788, f2c: 0.1374
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 3 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 25.9409 1 3.52e-07 *** speed_cond_num 0.0153 1 0.9016
group_char 0.4809 2 0.7863
speed_cond_num:group_char 1.9872 2 0.3702
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0063, r2c: 0.9397
f2m: 0.0063, f2c: 0.0407
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 3 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 48.9387 1 2.641e-12 ** speed_cond_num 3.9981 1 0.04555
group_char 4.6663 2 0.09699 .
speed_cond_num:group_char 3.0492 2 0.21771
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0465, r2c: 0.9611
f2m: 0.0488, f2c: 0.1552
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 4 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 14.5013 1 0.0001401 *** speed_cond_num 0.3890 1
0.5328424
group_char 3.5262 2 0.1715120
speed_cond_num:group_char 0.5283 2 0.7678607
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0441, r2c: 0.8823
f2m: 0.0462, f2c: 0.0194
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 4 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 58.7006 1 1.836e-14 *** speed_cond_num 0.5998 1 0.4387
group_char 0.7858 2 0.6751
speed_cond_num:group_char 2.6974 2 0.2596
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0262, r2c: 0.9457
f2m: 0.0269, f2c: 0.0724
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 4 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 50.4058 1 1.25e-12 *** speed_cond_num 1.6877 1 0.19390
group_char 1.9520 2 0.37681
speed_cond_num:group_char 5.7713 2 0.05582 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0219, r2c: 0.9644
f2m: 0.0224, f2c: 0.1057
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 5 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 19.9026 1 8.149e-06 speed_cond_num 12.5026 1
0.0004064 group_char 7.5501 2 0.0229354 *
speed_cond_num:group_char 0.7708 2 0.6801699
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.1183, r2c: 0.8683
f2m: 0.1342, f2c: 0.2763
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 5 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 32.6656 1 1.095e-08 *** speed_cond_num 0.7911 1 0.3738
group_char 0.4295 2 0.8067
speed_cond_num:group_char 0.4801 2 0.7866
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0144, r2c: 0.8884
f2m: 0.0146, f2c: 0.0291
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 5 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 15.8035 1 7.027e-05 ** speed_cond_num 4.3124 1 0.03784
group_char 3.7849 2 0.15070
speed_cond_num:group_char 1.8483 2 0.39687
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0495, r2c: 0.951
f2m: 0.0521, f2c: 0.1902
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 6 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 9.2903 1 0.002304 ** speed_cond_num 1.0219 1 0.312067
group_char 24.6432 2 4.454e-06 *** speed_cond_num:group_char 4.9017 2
0.086222 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.1866, r2c: 0.884
f2m: 0.2294, f2c: 0.0265
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 6 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 106.0081 1 <2e-16 *** speed_cond_num 1.2841 1
0.2571
group_char 1.9829 2 0.3710
speed_cond_num:group_char 0.1969 2 0.9062
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0297, r2c: 0.9304
f2m: 0.0306, f2c: 0.0356
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 6 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 105.6755 1 < 2e-16 *** speed_cond_num 1.4872 1
0.22265
group_char 4.6965 2 0.09554 .
speed_cond_num:group_char 0.3149 2 0.85430
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0575, r2c: 0.9277
f2m: 0.061, f2c: 0.0354
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 7 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 18.4751 1 1.721e-05 * speed_cond_num 6.7636 1
0.009304 group_char 3.8194 2 0.148125
speed_cond_num:group_char 0.1252 2 0.939317
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0751, r2c: 0.8415
f2m: 0.0811, f2c: 0.1051
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 7 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 23.9868 1 9.7e-07 *** speed_cond_num 1.6929 1 0.1932
group_char 0.3818 2 0.8262
speed_cond_num:group_char 1.2749 2 0.5286
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0039, r2c: 0.9522
f2m: 0.0039, f2c: 0.0416
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 7 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 23.8188 1 1.058e-06 *** speed_cond_num 1.7197 1
0.18973
group_char 5.7335 2 0.05688 .
speed_cond_num:group_char 4.6217 2 0.09918 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0623, r2c: 0.966
f2m: 0.0665, f2c: 0.1271
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 8 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 40.7377 1 1.741e-10 *** speed_cond_num 2.7071 1 0.09990
.
group_char 5.1614 2 0.07572 .
speed_cond_num:group_char 4.7459 2 0.09321 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0506, r2c: 0.8226
f2m: 0.0533, f2c: 0.1495
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 8 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 16.4763 1 4.926e-05 *** speed_cond_num 0.5693 1 0.4505
group_char 0.5292 2 0.7675
speed_cond_num:group_char 2.0604 2 0.3569
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0042, r2c: 0.8798
f2m: 0.0043, f2c: 0.0447
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 8 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 33.3222 1 7.809e-09 ** speed_cond_num 4.1275 1 0.04219
group_char 0.6329 2 0.72872
speed_cond_num:group_char 1.3675 2 0.50472
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0141, r2c: 0.9598
f2m: 0.0143, f2c: 0.0665
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 9 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 7.1421 1 0.007529 ** speed_cond_num 0.2523 1 0.615476
group_char 5.5227 2 0.063206 . speed_cond_num:group_char 0.9808 2
0.612367
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0938, r2c: 0.9173
f2m: 0.1035, f2c: 0.0455
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 9 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 27.9945 1 1.217e-07 *** speed_cond_num 0.3816 1 0.5367
group_char 3.0279 2 0.2200
speed_cond_num:group_char 2.1352 2 0.3438
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0541, r2c: 0.9573
f2m: 0.0572, f2c: 0.0909
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 9 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 27.1407 1 1.892e-07 ** speed_cond_num 2.0601 1
0.15120
group_char 1.0383 2 0.59502
speed_cond_num:group_char 6.3718 2 0.04134
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0249, r2c: 0.9209
f2m: 0.0255, f2c: 0.2708
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 10 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 0.1619 1 0.687417
speed_cond_num 9.0591 1 0.002614 group_char 13.2771 2 0.001309
speed_cond_num:group_char 11.7551 2 0.002802 ** — Signif.
codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’
1
[1] “intercept model” r2m: 0.3287, r2c: 0.8879
f2m: 0.4897, f2c: 0.4365
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 10 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 32.2899 1 1.328e-08 *** speed_cond_num 0.7059 1 0.4008
group_char 0.4610 2 0.7941
speed_cond_num:group_char 3.4828 2 0.1753
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0028, r2c: 0.914
f2m: 0.0028, f2c: 0.0637
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 10 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 20.2703 1 6.724e-06 *** speed_cond_num 2.5618 1
0.10948
group_char 1.1013 2 0.57657
speed_cond_num:group_char 4.6616 2 0.09722 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0165, r2c: 0.9394
f2m: 0.0168, f2c: 0.0712
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 11 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 16.3745 1 5.198e-05 *** speed_cond_num 0.0108 1
0.91719
group_char 0.5277 2 0.76808
speed_cond_num:group_char 5.2205 2 0.07352 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0103, r2c: 0.824
f2m: 0.0104, f2c: 0.0788
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 11 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 18.1307 1 2.062e-05 ** speed_cond_num 2.1156 1
0.14581
group_char 7.3875 2 0.02488
speed_cond_num:group_char 0.0764 2 0.96251
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.1286, r2c: 0.9518
f2m: 0.1476, f2c: 0.0629
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 11 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 21.3393 1 3.848e-06 ** speed_cond_num 4.0666 1 0.04374
group_char 0.1108 2 0.94610
speed_cond_num:group_char 1.5971 2 0.44997
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0157, r2c: 0.9293
f2m: 0.016, f2c: 0.1783
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 12 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 8.8009 1 0.003011 ** speed_cond_num 0.6908 1 0.405877
group_char 1.3827 2 0.500889
speed_cond_num:group_char 6.2166 2 0.044677 * — Signif. codes: 0
‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0214, r2c: 0.9263
f2m: 0.0219, f2c: 0.1351
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 12 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 79.1451 1 <2e-16 *** speed_cond_num 2.6341 1 0.1046
group_char 1.5873 2 0.4522
speed_cond_num:group_char 1.1710 2 0.5568
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0158, r2c: 0.9496
f2m: 0.0161, f2c: 0.0365
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 12 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 72.6717 1 <2e-16 *** speed_cond_num 0.4522 1 0.5013
group_char 0.1234 2 0.9402
speed_cond_num:group_char 3.5218 2 0.1719
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0037, r2c: 0.9337
f2m: 0.0037, f2c: 0.077
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 13 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 4.3913 1 0.03612 speed_cond_num 4.3047 1 0.03801
group_char 0.6109 2 0.73681
speed_cond_num:group_char 1.6212 2 0.44458
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0149, r2c: 0.8941
f2m: 0.0151, f2c: 0.1518
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 13 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 2.3681 1 0.12383
speed_cond_num 0.2698 1 0.60346
group_char 6.0790 2 0.04786 * speed_cond_num:group_char 1.9850 2
0.37065
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.1126, r2c: 0.9576
f2m: 0.1269, f2c: 0.0474
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 13 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 14.2172 1 0.0001629 *** speed_cond_num 0.8662 1
0.3520098
group_char 2.0633 2 0.3564231
speed_cond_num:group_char 0.3741 2 0.8294066
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0527, r2c: 0.9283
f2m: 0.0557, f2c: 0.072
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 3 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 13.1923 1 0.0002811 speed_cond_num 23.6036 1
1.184e-06 group_char 5.5658 2 0.0618576 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0716, r2c: 0.8912
f2m: 0.0772, f2c: 0.1259
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 3 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 30.4952 1 3.347e-08 ** speed_cond_num 4.0468 1 0.04425
group_char 0.3996 2 0.81890
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0059, r2c: 0.9396
f2m: 0.0059, f2c: 0.0404
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 3 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 54.6565 1 1.435e-13 speed_cond_num 28.2573 1
1.062e-07 group_char 3.4298 2 0.18
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0461, r2c: 0.9609
f2m: 0.0483, f2c: 0.1495
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 4 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 15.9077 1 6.651e-05 *** speed_cond_num 0.9343 1 0.3338
group_char 3.4467 2 0.1785
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0439, r2c: 0.8831
f2m: 0.0459, f2c: 0.0258
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 4 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 66.0381 1 4.423e-16 * speed_cond_num 9.0987 1
0.002558 group_char 1.7591 2 0.414976
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0257, r2c: 0.9455
f2m: 0.0264, f2c: 0.0684
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 4 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 54.7924 1 1.34e-13 speed_cond_num 12.4383 1
0.0004206 group_char 1.4071 2 0.4948224
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0211, r2c: 0.9637
f2m: 0.0216, f2c: 0.0856
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 5 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 20.539 1 5.844e-06 speed_cond_num 46.049 1
1.153e-11 group_char 7.075 2 0.02909 *
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.118, r2c: 0.8691
f2m: 0.1338, f2c: 0.2837
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 5 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 35.4282 1 2.646e-09 *** speed_cond_num 1.8843 1 0.1698
group_char 0.8828 2 0.6431
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0142, r2c: 0.8892
f2m: 0.0144, f2c: 0.0369
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 5 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 18.8268 1 1.431e-05 speed_cond_num 28.7503 1
8.234e-08 group_char 2.8305 2 0.2429
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0492, r2c: 0.9511
f2m: 0.0517, f2c: 0.1907
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 6 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 13.5089 1 0.0002374 speed_cond_num 0.0015 1
0.9691485
group_char 20.0355 2 4.46e-05 — Signif. codes: 0
‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.1849, r2c: 0.8825
f2m: 0.2269, f2c: 0.0132
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 6 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 113.8624 1 < 2e-16 ** speed_cond_num 5.4069 1 0.02006
group_char 2.4428 2 0.29482
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0297, r2c: 0.9309
f2m: 0.0306, f2c: 0.0431
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 6 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 113.5078 1 < 2e-16 ** speed_cond_num 5.4825 1 0.01921
group_char 4.9820 2 0.08283 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0575, r2c: 0.9282
f2m: 0.061, f2c: 0.0423
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 7 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 21.5834 1 3.388e-06 speed_cond_num 16.9237 1
3.891e-05 group_char 4.9197 2 0.08545 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0751, r2c: 0.8429
f2m: 0.0812, f2c: 0.115
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 7 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 24.4131 1 7.774e-07 *** speed_cond_num 3.2714 1 0.0705
.
group_char 0.1914 2 0.9088
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0036, r2c: 0.9523
f2m: 0.0036, f2c: 0.0453
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 7 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 26.9043 1 2.138e-07 speed_cond_num 15.5857 1
7.885e-05 group_char 4.0288 2 0.1334
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0617, r2c: 0.9656
f2m: 0.0658, f2c: 0.111
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 8 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 38.9553 1 4.336e-10 speed_cond_num 21.4540 1
3.624e-06 group_char 2.6385 2 0.2673
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0475, r2c: 0.8199
f2m: 0.0499, f2c: 0.1322
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 8 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 16.6351 1 4.53e-05 *** speed_cond_num 3.7470 1 0.0529
.
group_char 0.1155 2 0.9439
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0033, r2c: 0.8797
f2m: 0.0033, f2c: 0.0434
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 8 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 33.7704 1 6.201e-09 * speed_cond_num 8.5527 1
0.00345 group_char 0.8653 2 0.64879
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0139, r2c: 0.9599
f2m: 0.0141, f2c: 0.0696
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 9 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 6.8021 1 0.009105 ** speed_cond_num 2.6484 1 0.103654
group_char 4.7702 2 0.092078 . — Signif. codes: 0 ‘’ 0.001
’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0934, r2c: 0.9178
f2m: 0.103, f2c: 0.0527
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 9 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 31.5139 1 1.98e-08 * speed_cond_num 6.7540 1
0.009354 group_char 2.4846 2 0.288713
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0536, r2c: 0.9572
f2m: 0.0566, f2c: 0.0892
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 9 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 34.6336 1 3.980e-09 speed_cond_num 24.2111 1
8.634e-07 group_char 0.5121 2 0.7741
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.022, r2c: 0.9182
f2m: 0.0225, f2c: 0.2286
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 10 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 0.0242 1 0.8764
speed_cond_num 33.4908 1 7.160e-09 group_char 18.8377 2
8.118e-05 — Signif. codes: 0 ‘’ 0.001
’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.3205, r2c: 0.8781
f2m: 0.4717, f2c: 0.3204
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 10 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 29.1430 1 6.723e-08 *** speed_cond_num 0.4491 1 0.5028
group_char 0.0227 2 0.9887
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 8e-04, r2c: 0.9128
f2m: 8e-04, f2c: 0.0487
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 10 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 17.7986 1 2.456e-05 *** speed_cond_num 0.1527 1 0.6959
group_char 0.5852 2 0.7463
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0146, r2c: 0.9379
f2m: 0.0148, f2c: 0.0458
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 11 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 13.9319 1 0.0001895 *** speed_cond_num 3.6638 1 0.0556083
.
group_char 0.1487 2 0.9283667
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0058, r2c: 0.8199
f2m: 0.0058, f2c: 0.0546
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 11 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 19.3132 1 1.109e-05 speed_cond_num 7.4046 1
0.006506 group_char 7.7061 2 0.021214
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.1286, r2c: 0.9524
f2m: 0.1476, f2c: 0.076
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 11 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 24.8404 1 6.228e-07 speed_cond_num 21.8450 1
2.956e-06 group_char 0.4138 2 0.8131
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0152, r2c: 0.9295
f2m: 0.0154, f2c: 0.1806
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 12 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 6.0492 1 0.01391 *
speed_cond_num 18.6504 1 1.57e-05 *** group_char 1.1359 2 0.56668
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0198, r2c: 0.9247
f2m: 0.0202, f2c: 0.1124
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 12 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 80.2372 1 < 2e-16 *** speed_cond_num 3.5566 1 0.05931
.
group_char 1.1213 2 0.57083
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0156, r2c: 0.9498
f2m: 0.0159, f2c: 0.0402
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 12 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 82.9493 1 < 2.2e-16 * speed_cond_num 9.4525 1
0.002109 group_char 0.0460 2 0.977258
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0028, r2c: 0.9332
f2m: 0.0029, f2c: 0.0689
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 13 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 4.8762 1 0.0272294 *
speed_cond_num 12.0244 1 0.0005251 *** group_char 0.2240 2
0.8940312
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0138, r2c: 0.8943
f2m: 0.014, f2c: 0.154
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 13 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 3.4075 1 0.06490 . speed_cond_num 1.0684 1 0.30132
group_char 4.9318 2 0.08493 . — Signif. codes: 0 ‘’ 0.001
’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.1121, r2c: 0.9576
f2m: 0.1263, f2c: 0.047
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 13 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 16.0226 1 6.259e-05 ** speed_cond_num 4.9565 1 0.02599
group_char 2.1220 2 0.34612
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0526, r2c: 0.9292
f2m: 0.0555, f2c: 0.0867
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 3 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 8.2907 1 0.003985 ** speed_cond_num 23.6036 1 1.184e-06 ***
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0088, r2c: 0.8888
f2m: 0.0089, f2c: 0.1009
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 3 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 80.3627 1 < 2e-16 ** speed_cond_num 4.0468 1 0.04425
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 8e-04, r2c: 0.9381
f2m: 8e-04, f2c: 0.0138
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 3 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 199.234 1 < 2.2e-16 speed_cond_num 28.257 1
1.062e-07 — Signif. codes: 0 ‘’ 0.001
’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0038, r2c: 0.9599
f2m: 0.0038, f2c: 0.1213
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 4 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 17.6849 1 2.607e-05 *** speed_cond_num 0.9343 1 0.3338
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 4e-04, r2c: 0.8801
f2m: 4e-04, f2c: 1e-04
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 4 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 129.7694 1 < 2.2e-16 * speed_cond_num 9.0987 1
0.002558 — Signif. codes: 0 ‘’ 0.001 ’’
0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0019, r2c: 0.9439
f2m: 0.0019, f2c: 0.0393
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 4 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 165.759 1 < 2.2e-16 speed_cond_num 12.438 1
0.0004206 — Signif. codes: 0 ‘’ 0.001
’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0017, r2c: 0.9627
f2m: 0.0017, f2c: 0.0554
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 5 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 16.078 1 6.080e-05 speed_cond_num 46.049 1
1.153e-11 — Signif. codes: 0 ‘’ 0.001
’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0259, r2c: 0.8657
f2m: 0.0266, f2c: 0.2511
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 5 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 87.1467 1 <2e-16 *** speed_cond_num 1.8843 1 0.1698
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 9e-04, r2c: 0.8857
f2m: 9e-04, f2c: 0.0054
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 5 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 84.714 1 < 2.2e-16 speed_cond_num 28.750 1
8.234e-08 — Signif. codes: 0 ‘’ 0.001
’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0061, r2c: 0.9495
f2m: 0.0061, f2c: 0.1544
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 88.7238 1 <2e-16 *** speed_cond_num 0.0015 1 0.9691
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0, r2c: 0.8805
f2m: 0, f2c: -0.0038
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 6 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 244.0644 1 < 2e-16 ** speed_cond_num 5.4069 1 0.02006
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0012, r2c: 0.9293
f2m: 0.0012, f2c: 0.0188
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 6 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 220.6644 1 < 2e-16 ** speed_cond_num 5.4825 1 0.01921
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0013, r2c: 0.9265
f2m: 0.0013, f2c: 0.0192
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 7 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 20.194 1 6.997e-06 speed_cond_num 16.924 1
3.891e-05 — Signif. codes: 0 ‘’ 0.001
’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.011, r2c: 0.8388
f2m: 0.0112, f2c: 0.0864
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 7 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 56.9891 1 4.382e-14 *** speed_cond_num 3.2714 1 0.0705
.
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 7e-04, r2c: 0.9508
f2m: 7e-04, f2c: 0.0124
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 7 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 109.017 1 < 2.2e-16 speed_cond_num 15.586 1
7.885e-05 — Signif. codes: 0 ‘’ 0.001
’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0022, r2c: 0.9645
f2m: 0.0022, f2c: 0.0786
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 8 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 57.235 1 3.867e-14 speed_cond_num 21.454 1
3.624e-06 — Signif. codes: 0 ‘’ 0.001
’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0151, r2c: 0.8153
f2m: 0.0153, f2c: 0.1041
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 8 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 44.739 1 2.252e-11 *** speed_cond_num 3.747 1 0.0529 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0018, r2c: 0.8763
f2m: 0.0018, f2c: 0.0144
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 8 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 109.9469 1 < 2e-16 * speed_cond_num 8.5527 1
0.00345 — Signif. codes: 0 ‘’ 0.001 ’’
0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0013, r2c: 0.9587
f2m: 0.0013, f2c: 0.0383
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 9 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 14.0193 1 0.0001809 *** speed_cond_num 2.6484 1
0.1036535
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0013, r2c: 0.9146
f2m: 0.0013, f2c: 0.013
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 9 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 80.778 1 < 2.2e-16 * speed_cond_num 6.754 1
0.009354 — Signif. codes: 0 ‘’ 0.001 ’’
0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0017, r2c: 0.9553
f2m: 0.0017, f2c: 0.0439
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 9 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 80.972 1 < 2.2e-16 speed_cond_num 24.211 1
8.634e-07 — Signif. codes: 0 ‘’ 0.001
’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0118, r2c: 0.9146
f2m: 0.012, f2c: 0.1765
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 10 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 9.5082 1 0.002046 ** speed_cond_num 33.4908 1 7.16e-09 *** —
Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’
0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0278, r2c: 0.8748
f2m: 0.0286, f2c: 0.2863
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 10 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 56.8530 1 4.696e-14 *** speed_cond_num 0.4491 1 0.5028
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 3e-04, r2c: 0.9081
f2m: 3e-04, f2c: -0.0042
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 10 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 29.5478 1 5.455e-08 *** speed_cond_num 0.1527 1 0.6959
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 1e-04, r2c: 0.9346
f2m: 1e-04, f2c: -0.007
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 11 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 37.1953 1 1.069e-09 *** speed_cond_num 3.6638 1 0.05561
.
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0034, r2c: 0.8135
f2m: 0.0034, f2c: 0.0184
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 11 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 105.0066 1 < 2.2e-16 * speed_cond_num 7.4046 1
0.006506 — Signif. codes: 0 ‘’ 0.001 ’’
0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0018, r2c: 0.9509
f2m: 0.0018, f2c: 0.0421
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 11 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 63.614 1 1.513e-15 speed_cond_num 21.845 1
2.956e-06 — Signif. codes: 0 ‘’ 0.001
’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0079, r2c: 0.9267
f2m: 0.0079, f2c: 0.1367
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 12 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 17.896 1 2.333e-05 speed_cond_num 18.650 1
1.570e-05 — Signif. codes: 0 ‘’ 0.001
’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0051, r2c: 0.9227
f2m: 0.0051, f2c: 0.0832
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 12 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 166.8652 1 < 2e-16 *** speed_cond_num 3.5566 1 0.05931
.
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 6e-04, r2c: 0.9484
f2m: 6e-04, f2c: 0.0122
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 12 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 198.0068 1 < 2.2e-16 * speed_cond_num 9.4525 1
0.002109 — Signif. codes: 0 ‘’ 0.001 ’’
0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0023, r2c: 0.9313
f2m: 0.0023, f2c: 0.0399
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in theta_avg_power for Cluster: 13 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 18.002 1 2.207e-05 speed_cond_num 12.024 1
0.0005251 — Signif. codes: 0 ‘’ 0.001
’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0088, r2c: 0.8889
f2m: 0.0089, f2c: 0.0976
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in alpha_avg_power for Cluster: 13 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 33.2238 1 8.214e-09 *** speed_cond_num 1.0684 1 0.3013
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 3e-04, r2c: 0.9557
f2m: 3e-04, f2c: 9e-04
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]
| Changes in beta_avg_power for Cluster: 13 | |||
| Characteristic | Beta (95% CI)1 | p-value | q-value2 |
|---|---|---|---|
| 1 CI = Confidence Interval | |||
| 2 False discovery rate correction for multiple testing | |||
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 31.5165 1 1.978e-08 ** speed_cond_num 4.9565 1 0.02599
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
[1] “intercept model” r2m: 0.0024, r2c: 0.9257
f2m: 0.0024, f2c: 0.0352
[[1]]
[[2]] [[2]]$subj_char
[[3]]
[[4]]